Resting-state functional connectivity and structural differences between smokers and healthy non-smokers

Previous studies have shown an association between cigarette use and altered resting-state functional connectivity (rsFC) in many large-scale networks, sometimes complemented by measures of cortical atrophy. In this study, we aimed to further explore the neural differences between smokers and healthy non-smokers through the integration of functional and structural analyses. Imaging data of fifty-two smokers and forty-five non-smokers were analyzed through an independent component analysis for group differences in rsFC. Smokers showed lower rsFC within the dorsal attention network (DAN) in the left superior and middle frontal gyrus and left superior division of the lateral occipital cortex compared to non-smokers; moreover, cigarette use was found to be associated with reduced grey matter volume in the left superior and middle frontal gyrus and right orbitofrontal cortex, partly overlapping with functional findings. Within smokers, daily cigarette consumption was positively associated with increased rsFC within the cerebellar network and the default mode network and decreased rsFC within the visual network and the salience network, while carbon monoxide level showed a positive association with increased rsFC within the sensorimotor network. Our results suggest that smoking negatively impacts rsFC within the DAN and that changes within this network might serve as a circuit-based biomarker for structural deficits.


Design
Following the study instructions, participants completed the German versions of the Barrett Impulsiveness Scale (BIS-11) 23 , the Alcohol Use Disorders Identification Test (AUDIT) 24 and the Fagerström Test for Nicotine Dependence (FTND) 26 .Subsequently, participants completed the Stop Signal Task (SST; data not included here) followed by a short break, during which smokers had a cigarette.Smokers were then asked to provide breath CO levels using a Smokerlyzer (Bedfont Scientific Ltd., Harrietsham, UK).The following MRI session included resting-state functional magnetic resonance imaging (fMRI), during which participants were instructed to look at a fixation cross and let their mind wander, and structural MRI.Table 1.Demographic characteristics of smokers and healthy non-smokers.All data are reported with mean ± standard deviation.BIS Barratt Impulsiveness Scale, FTND Fagerström Test for Nicotine Dependence, AUDIT alcohol use disorders identification test, CO carbon monoxide, ppm parts per million.*Information on the number of years smoking was available for forty-three out of fifty-two smokers.

MRI data acquisition
MRI data were collected using a 3-Tesla Siemens PRISMA scanner (Siemens Medical Systems, Erlangen, Germany) located in the Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University Hospital.T1-weighted structural images were acquired with a 20-channel head coil by means of a three-dimensional magnetization-prepared rapid acquisition gradient echo image (MPRAGE) sequence (voxel size: 1 × 1 × 1 mm 3 ; 256 × 256 matrix; field of view [FoV]: 256 × 256 mm 2 ; 176 volumes; time repetition [TR] = 2300 ms; time echo [TE] = 2.98 ms; flip angle = 9°).Resting-state data were acquired using an echoplanar imaging (EPI) sequence with 34 slices.In-plane resolution of the slices was 64 × 64 pixels with a total field of view of 192 mm 2 and a voxel-size of 3 × 3 × 3 mm 3 .Images were acquired with a TR of 2000 ms, a TE of 28 ms and a flip-angle of 77° in an interleaved sequence.

MRI data preprocessing
Functional and anatomical data were preprocessed through CONN 27 release 22.a 28 toolbox implemented in SPM 29 release 12.7771, using a flexible preprocessing pipeline 30 including realignment with correction of susceptibility distortion interactions, outlier detection, direct segmentation and MNI-space normalization, and smoothing.Functional data were realigned using SPM realign & unwarp procedure 31 , where all scans were coregistered to a reference image (first scan of the first session) using a least squares approach and a 6 parameter (rigid body) transformation 32 , and resampled using b-spline interpolation to correct for motion and magnetic susceptibility interactions.Potential outlier scans were identified using ART 33 as acquisitions with framewise displacement above 0.9 mm or global BOLD signal changes above 5 standard deviations 34,35 , and a reference BOLD image was computed for each subject by averaging all scans excluding outliers.Functional and anatomical data were normalized into standard MNI space, segmented into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) tissue classes, and resampled to 3 mm isotropic voxels following a direct normalization procedure 35,36 using SPM unified segmentation and normalization algorithm 37,38 with the default IXI-549 tissue probability map template.Last, functional data were smoothed using spatial convolution with a Gaussian kernel of 8 mm full-width-half-maximum (FWHM).In addition, functional data were denoised using a standard denoising pipeline 39 including the regression of potential confounding effects characterized by white matter timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), motion parameters and their first order derivatives (12 factors) 40 , outlier scans (below 102 factors) 33 , session effects and their first order derivatives (2 factors), and linear trends (2 factors) within each functional run, followed by bandpass frequency filtering of the BOLD timeseries 41 between 0.008 Hz and 0.09 Hz.CompCor 42,43 noise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average, motion parameters, and outlier scans within each subject's eroded segmentation masks.From the number of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD signal after denoising were estimated to range from 36.7 to 70.2 (average 69.1) across all subjects 34 .

First-level analysis
Group-level independent component analyses (group-ICA 44 ) were performed to estimate 20 temporally coherent networks from the fMRI data combined across all subjects.The suggested number of components was determined by the developers of the CONN toolbox to ensure sufficient characterization and distinct separation of the represented components.This is achieved by matching the component to a network template through an automated spatial correlation process 27 and is in line with previous studies using low model order analysis [45][46][47] .The BOLD signal from every timepoint and voxel in the brain was concatenated across subjects and conditions along the temporal dimension.A singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with 64 components separately for each subject was used as a subject-specific dimensionality reduction step.
The dimensionality of the concatenated data was further reduced using a singular value decomposition (grouplevel SVD) with 20 components, and a fast-ICA fixed-point algorithm 48 with hyperbolic tangent (G1) contrast function was used to identify spatially independent group-level networks from the resulting components.Last, GICA3 back-projection 49 was used to compute ICA maps associated with these same networks separately for each individual subject.

Group-level analyses
Group-level analyses were performed using a General Linear Model (GLM 50 ).For each individual voxel a separate GLM was estimated, with first-level connectivity measures at this voxel as dependent variables (one independent sample per subject), group as the independent variable, and age and total intracranial volume (TIV) as covariates.Voxel-level hypotheses were evaluated using multivariate parametric statistics with random-effects across subjects and sample covariance estimation across multiple measurements.Inferences were performed at the level of individual clusters (groups of contiguous voxels).Cluster-level inferences were based on parametric statistics from Gaussian Random Field Theory 51,52 .Results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold, and a family-wise corrected p-FWE < 0.05 cluster-size threshold 53 .

Structural analysis
All T1-weighted images were first visually scanned for imaging artifacts or any other abnormalities.One subject had to be excluded from further analyses due to a structural abnormality.T1-weighted MRI scans were then analyzed with the Computational Anatomy Toolbox (CAT12; www.neuro.uni-jena.de/ cat/) 54 implemented in SPM 29 .Image preprocessing followed the default settings of CAT12; all images were segmented into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) components and spatially normalized within the www.nature.com/scientificreports/Montreal Neurological Institute (MNI) template.Afterward, TIV was estimated, and the homogeneity of the sample was checked for using the unsmoothed segmentations.Quartic mean z-scores were corrected for TIV by global scaling.Finally, spatial smoothing with an 8 mm full-width-half-maximum (FWHM) Gaussian kernel was applied to minimize the potential misalignment and enhance the signal-to-noise ratio.To examine the difference in GM volume between smokers and non-smokers, an independent sample t-test was conducted, including age as a covariate of no interest, and correcting for TIV to account for different brain sizes.Multiple regression analyses were performed within the smokers' group to detect relations between GM volume and magnitude of tobacco consumption, here operationalized as the number of daily cigarettes and the CO level.For all analyses, absolute masking with a threshold of 0.1 was applied to ensure that only the intended tissue type was analyzed.
A statistical threshold of p < 0.05 corrected for multiple comparisons using the Family-wise Error (FWE) rate on cluster-level was also applied.

Functional connectivity (ICA)
A spatial match-to-template revealed correlation (Pearson's r) values for each of the 20 estimated independent components with resting-state networks within the CONN toolbox (i.e., salience, fronto-parietal, language, cerebellar, visual, default mode, dorsal attention, sensorimotor).Based on the highest correlation between each group spatial map and templates of resting-state networks, eight components-one for each resting-state network-were identified and extracted using a group-ICA (salience network: r = 0.37; fronto-parietal network: r = 0.26; language network: r = 0.34; cerebellar network: r = 0.31; visual network: r = 0.51; default mode network: r = 0.36; dorsal attention network: r = 0.54; sensorimotor network: r = 0.46; see Fig. 1).They were then compared between the two groups (smokers vs non-smokers) using an independent sample t-test.Smokers showed reduced connectivity within the dorsal attention network (DAN) in the left superior and middle frontal gyrus (MNI coordinates: x = −26, y = + 12, z = + 62, size = 522; T(94) = -5.26,p-FWE cluster-level < 0.001) and left superior division of the lateral occipital cortex (MNI coordinates: x = −36, y = -66, z = + 42, size = 257; T(94) = -4.99,p-FWE cluster-level < 0.001), as shown in Fig. 2. Within smokers, higher levels of CO were associated with increased connectivity within the sensorimotor network (SMN) in the left central opercular cortex.Smokers with higher consumption of daily cigarettes, on the other hand, showed increased connectivity within the cerebellar network (CN) in the left and right cerebellum and within the default mode network (DMN) in the left frontal pole; they also showed decreased connectivity within the visual network (VN) in the left paracingulate gyrus and within the salience network (SN) in the anterior cingulate gyrus (see Table 2).Moreover, a positive significant correlation between CO levels and daily cigarette consumption was found (ρ = 0.417, p = 0.004).3.Moreover, increasing age in smokers was associated with GM volume loss in widespread and extended brain regions.On the other hand, non-smokers showed a decreased GM volume with increasing age in a few areas (see Table 3).No regions were found where the GM volume was significantly correlated with the number of daily cigarettes, nor with the CO level.

Discussion
In this study, we investigated the effect of cigarette smoking on the functional connectivity of eight standard resting-state networks, as well as structural GM differences between smokers and healthy non-smokers.Our results showed that smokers had decreased functional connectivity within the dorsal attention network (DAN) in the left superior and middle frontal gyrus and lateral occipital cortex compared to non-smokers, and these   www.nature.com/scientificreports/regions also partly overlapped with reduced GM volume in cigarette users.We also investigated the association between increasing age and GM volume loss in smokers and non-smokers separately and the effects of CO level and daily cigarette consumption on rsFC within the smokers' group.When inspecting the resting-state networks identified via the group-ICA we found reduced functional connectivity in smokers solely within the DAN, specifically in the left superior and middle frontal gyrus and left superior division of the lateral occipital cortex, in comparison to healthy non-smokers.The DAN is a large-scale brain network that, among other functions, encodes and maintains preparatory signals, participates in the control and initiation of voluntary movements, and modulates top-down activity during the spatial orienting of attention 55 .Previous studies have found that cigarette smoking has a negative impact on prefrontal attentional network functioning, causing less extended network activity in different areas, such as the supplementary motor area, anterior cingulate cortex, dorsolateral prefrontal cortex (including the middle frontal gyrus), and parietal cortex 56 .In fact, even though acute administration of nicotine is known to have positive effects on cognitive functioning, such as fine motor skills, working memory, and selective attention 5,57 , chronic cigarette smoking, on the other hand, is associated with lower cortical perfusion in multiple brain regions 58 .Since every study has a different procedure-smokers may or may not be allowed to smoke a cigarette before entering the scanner-it can be difficult to differentiate between acute and chronic effects of nicotine.Moreover, it has been consistently reported that chronic smokers might be subject to the neurotoxic effects of nicotine and other chemical compounds (e.g., carbon monoxide, free radicals, nitrogen oxides) 59 , which are closely related to GM volume loss 13 .Several studies have previously reported reduced GM volume in specific structures of the left hemisphere, including the insula, dorsal anterior cingulate cortex, and superior frontal gyrus 7,10,60,61 .Our results show overlapping functional and structural brain differences in smokers, with reduced GM volume and functional connectivity in the left superior and middle frontal gyrus, a region involved in, among other functions, response inhibition and attentional reorienting 62,63 .This overlap indicates that cigarette smoking may modify functional connectivity in the prefrontal areas, and goes along with parallel GM volume loss in that same region; in fact, it has been previously described how distributed network maps can mirror the cortical atrophy patterns seen in different forms of neurodegeneration 64 .As earlier reported by Pariyadath et al. 65 , rsFC within the middle superior frontal gyrus has been identified as a discriminatory feature in a machine-learning classifier to distinguish between smokers and non-smokers, which could potentially be predictive of nicotine dependence; changes within this network might therefore serve as a circuit-based biomarker for future structural deficits 6 .Furthermore, we found that age seems to affect smokers and healthy non-smokers differently, although it must be mentioned that the mean age of both groups is moderately young, as reported in Table 1.Specifically, age among smokers has a large, widespread effect on GM volume loss, while among non-smokers this effect is limited to a few clusters.According to Durazzo et al. 66,67 , smokers showed greater volume loss of anterior frontal regions such as the rostral middle frontal gyrus, medial orbitofrontal cortex, pars triangularis, pars orbitalis, lateral orbitofrontal cortex, frontal pole, and posterior regions associated with neuroanatomic abnormalities in Alzheimer's disease, including the hippocampus (included all subregions), inferior parietal lobule, and middle temporal gyrus for each year of advancing age compared to healthy non-smokers.Our results seem to be in line with these previous findings.www.nature.com/scientificreports/Within the smokers' group, we discovered that breath CO concentration and daily cigarette consumption seem to measure different aspects of smoking behavior since they tend to have differential influences on rsFC.In our study, the CO level was associated with increased resting-state connectivity within the SMN, while daily cigarette consumption was positively associated with increased connectivity within the CN and the DMN, and negatively associated with the SN and the VN.Nevertheless, neither CO level nor daily cigarette consumption showed a significant effect on GM volume loss.While CO level has been reported as an immediate and effective measure of smoking status 68 , mostly related to state-dependent variables (e.g., puff volume 69 , inhalation pattern 70 , filter vent blocking 71 ), the number of daily cigarettes does not capture the effects of smoking topography 72 and vent blocking 71 .Moreover, CO level can vary according to pre-and post-cigarette measurements; this difference is known as CO boost, which has been shown to be positively associated with total and average puff volume, depth of inhalation, and puff duration 69,73 .On the other hand, daily cigarette consumption, being a self-report measure, solely relies on the participants' recalling; for this reason, it may be an inaccurate measure of smoking intensity 74 , but it is not susceptible to rapid changes in smoking behavior.Since they seem to affect different brain networks, both indicators should therefore be used to better assess tobacco consumption.
The following limitations of the study should be reported.First, since the sample is constituted of only males to increase the homogeneity of the sample itself, the results presented here are not generalizable to the female population.Secondly, measures of CO level were taken at different times during the day for each participant, and this may have contributed to higher variability in the smokers' breath CO concentration.Finally, it was not possible to make direct comparisons between smokers' and non-smokers' age groups but only to see the effect of age on both populations separately.Future studies should focus more on this aspect, using functional and structural analyses to investigate the effects of smoking-according to different variables, including smoking topography, filter vent blocking, and age-on rsFC and GM volume.

Conclusion
The present study shows that cigarette smokers, compared to non-smokers, exhibit decreased functional connectivity in the left superior and middle frontal gyrus and left lateral occipital cortex, regions that are heavily involved in attentional processes and response inhibition.Smokers also presented reduced GM volume in the left superior and middle frontal gyrus, showing overlap with rsFC findings, and in the right orbitofrontal cortex.Even though the causality of the relationship cannot be directly inferred, our results indicate that smoking might be negatively related to rsFC and GM volume, notably within prefrontal areas, thus offering further insight into the neural substrates of smoking behavior.

Table 2 .
Regions with altered functional connectivity in smokers associated to CO level and daily cigarette consumption.↑ indicates increased connectivity, while ↓ indicates decreased connectivity.L left, SMN sensorimotor network, CN cerebellar network, SN salience network, VN visual network, DMN default mode network, MNI montreal neurological institute, FEW family-wise error.Covariate Resting-state network Region Size MNI coordinates (x, y, z) Size p-FWE

Table 3 .
Regions with decreased grey matter volume associated with increasing age in smokers and healthy non-smokers.L left, R right, MNI montreal neurological institute, FEW family-wise error.